Protein-protein interactions

Protein–protein interactions (PPIs) represent a significant portion of functionally relevant biological interactions, and therefore potential therapeutic targets. Small molecules were traditionally used to target PPIs. However, many PPI surfaces lack binding pockets due to their large and flat structures. Antibodies can also be used to modulate PPIs, but they are expensive and not cell permeable. Linear peptides are less expensive to produce than antibodies and are generally more selective than small molecules, but they are limited by decreased stability and poor permeability. Modified peptides (peptidomimetics, e.g., cyclic peptides) can overcome these obstacles. Advantages of using cyclic peptidomimetics to modulate PPIs derive from their conformational constraint, which supports target specificity, cell permeability, and metabolic stability. Methods for rational design coupled with high-throughput techniques continue to support advances in the field. Further development of cyclic peptidomimetics to modulate PPIs will improve treatment of human diseases, such as cancer, infection, neurodegeneration, and autoim-munity. Here we describe several cyclic peptidomimetics that are currently used as drugs and many potential cyclic peptides PPI inhibitors in different stages of pre-clinical and clinical development. Further development of cyclic peptidomimetics to modulate PPIs will continue to improve treatment of human diseases, such as cancer, infection, neurodegeneration, and autoimmunity.

The VHL tumour suppressor gene codes for von-Hippel Lindau (VHL)
protein which acts by association with Elongin C and Hypoxia Inducing Factor-1
alpha (HIF). Two functional sub-domains in the protein model of VHL contain binding
sites for these proteins. Alpha domain of VHL protein contains the binding site for
Elongin C (tumorigenic cluster A) whereas; the beta domain contains the binding site
for HIF-1 alpha (tumorigenic cluster B). The complex formation between VHL, Elongin
C and HIF- 1 alpha protein is crucial for the regulation of ubiquitin dependent
proteolysis of oncogenic proteins. Henceforth defects in the complex formation may
lead to von-Hippel Lindau disease. A majority of VHL disease causing mutations
found to occur in these binding sites are highly conserved. In this computational analysis,
we predicted the functional effect of Single amino acid Polymorphisms (SAPs)
in VHL-interacting proteins with the aid of pathogenicity and protein stability predictors.
Furthermore, molecular dynamic behavior of the native and mutant protein complexes
were analyzed by using different parameters such as Root Mean Square Deviation,
Root Mean Square Fluctuation and Hydrogen bonding variations Molecular
dynamics studies were performed for five variants S111R, H115Q and W117C,
L158V and C162F of VHL protein that occur in the Elongin-C and HIF-1 alpha protein
binding domain. Through the molecular dynamics simulations analysis, we observed
that the stability and flexibility between VHL-Elongin-C and VHL-HIF-1
complexes were altered. Our strategy may be valuable for understanding SAP effects
on protein-protein interactions with function and their role in human genetic diseases,
linking structure function relationship and also for the development of novel pharmacological
strategies.

Several visualization tools for the mapping of protein-protein interactions have been developed in recent years. However, a systematic comparison of the virtues and limitations of different PPI visualization tools has not been carried out so far. In this study, we compare seven commonly used visualization tools, based on input and output file format, layout algorithm, database integration, Gene Ontology annotation and accessibility of each tool. The assessment was carried out based on brain disease datasets. Our suggested tools, NAViGaTOR, Cytoscape and Gephi perform competitively as PPI network visualization tools, can be a reference for future researches on PPI mapping and analysis.

Despite remarkable progress in proteomic methods, including improved detection limits and sensitivity, these methods have not yet been established in routine clinical practice. The main limitations, which prevent their integration into clinics, are high cost of equipment, the need for highly trained personnel, and last, but not least, the establishment of reliable and accurate protein biomarkers or panels of protein biomarkers for detection of neoplasms. Furthermore, the complexity and heterogeneity of most solid tumours present obstacles in the discovery of specific protein signatures, which could be used for early detection of cancers, for prediction of disease outcome, and for determining the response to specific therapies. However, cancer proteome, as the end-point of pathological processes that underlie cancer development and progression, could represent an important source for the discovery of new biomarkers and molecular targets for tailored therapies.

"Networks are mathematically directed (in practical applications also undirected) graphs and a graph is a one-dimensional abstract complex, i.e., a topological space. Network theory focuses on various topological structures and properties, dynamic properties, and functionality-topology relationship, etc. There are some common mathematical foundations, theories and methodology for network analysis, in which graph theory, statistics, and operational research, etc., are the fundamental sciences of network analysis. Various emerging biological networks, at both micro- and macro- levels, will provide numerous sources for the development of general network theory and methodology and also facilitate the development of theory and methodology of biological networks.

Biological network analysis is a fast moving science. Many core scientific issues, for example, ecological structure, co-evolution, co-extinction and biodiversity conservation in ecology, and cancer development and metabolic regulation in health science, etc., are expected to be addressed by network approaches and network analysis. Network analysis is becoming the core methodology to treat complex biological systems. As the fast development of this area, more and more papers on biological networks are published.

At present explosive numbers of quality papers on biological networks are being published each year. The initiation of the journal, Network Biology, will provide a public and unified platform for the publication of these studies. From this integrated and unique journal, researchers, university teachers and students will thoroughly have an in-depth and complete insight on knowledge, methodology and recent advances of biological networks.

In the view of system dynamics, biological networks are always self-organized systems with emergent, autonomous and adaptive properties. Their dynamics can be represented by agent-based modeling, individual-based modeling and some other methodologies like neural network modeling. Therefore, agent-based modeling, individual-based modeling, self-organization of biological systems, and neural network modeling, etc., fall into the aims and scope of the journal, Network Biology.

The NETWORK BIOLOGY (ISSN 2220-8879) is an open access, peer-reviewed online journal that considers scientific articles in all different areas of network biology. It is the transactions of the International Society of Network Biology.It dedicates to the latest advances in network biology. The goal of this journal is to keep a record of the state-of-the-art research and promote the research work in these fast moving areas. The topics to be covered by Network Biology include, but are not limited to:

•Theories, algorithms and programs of network analysis
•Innovations and applications of biological networks
•Dynamics, optimization and control of biological networks
•Ecological networks, food webs and natural equilibrium
•Co-evolution, co-extinction, biodiversity conservation
•Metabolic networks, protein-protein interaction networks, biochemical reaction networks, gene networks, transcriptional regulatory networks, cell cycle networks, phylogenetic networks, network motifs
•Physiological networks
•Network regulation of metabolic processes, human diseases and ecological systems
•Social networks, epidemiological networks
•System complexity, self-organized systems, emergence of biological systems, agent-based modeling, individual-based modeling, neural network modeling, and other network-based modeling, etc.
We are also interested in short communications that clearly address a specific issue or completely present a new ecological network, food web, or metabolic or gene network, etc.

Authors can submit their works to the email box of this journal, [email protected]. All manuscripts submitted to this journal must be previously unpublished and may not be considered for publication elsewhere at any time during review period of this journal. Authors are asked to read Author Guidelines before submitting manuscripts.

In addition to free submissions from authors around the world, special issues are also accepted. The organizer of a special issue can collect submissions (yielded from a research project, a research group, etc.) on a specific research topic, or submissions of a scientific conference for publication of special issue.

Network Biology 
ISSN 2220-8879
URL: http://www.iaees.org/publications/journals/nb/online-version.asp
RSS feed: http://www.iaees.org/publications/journals/nb/rss.xml
E-mail: [email protected]
Editor-in-Chief: WenJun Zhang"

Mustard oil is one of the main edible oil consumed in Bangladesh which market is entirely controlled by local producers. However, no study has been reported to assess the actual quality of these products yet. To evaluate this situation, an experimental study has been performed to detect the degree of different types of adulteration and to establish their relative contamination level in different brands of commercial mustard oil. Qualitative tests were performed for twelve samples of which eleven were arbitrarily chosen commercial samples and the rest was a raw sample produced from seed. These were investigated to find five common adulterants in mustard oil. Unfortunately, the presence of argemone oil and mineral oil were found in all the samples, however, the concentration of argemone oil was ten folds higher in some of the commercial oils compared to the raw oil. The appearance of turbidity in the case of mineral oil detection was quantified with the help of spectrophotometer and they varied from 74 to 1165 FAU. A wide range of variation in these values attributes to the variations in the processing steps of commercial mustard oil. Castor oil was present in some of the samples in varying amount. Of twelve samples, two contained remarkably high amount of castor oil, five contained trace amount and the rest was free from this contamination. Prohibited color was not found in any of the samples. HAZEN, pH and density data for all the oil samples were determined which ranged from 310 to 926 H,3.62 to 5.5 and 843.33 to 910 kg/m 3 respectively which might be attributed to different refining techniques. The results are very alarming as the presence of these adulterants endanger public health.

The traditional computational modeling of protein structure, dynamics, and interactions remains difficult for many protein systems. It is mostly due to the size of protein conformational spaces and required simulation time scales that are still too large to be studied in atomistic detail. Lowering the level of protein representation from all-atom to coarse-grained opens up new possibilities for studying protein systems. In this review we provide an overview of coarse-grained models focusing on their design, including choices of representation, models of energy functions, sampling of conformational space, and applications in the modeling of protein structure, dynamics, and interactions. A more detailed description is given for applications of coarse-grained models suitable for efficient combinations with all-atom simulations in multiscale modeling strategies.

The transcription factor STAT5b is a target for tumour therapy. We recently reported catechol bisphosphate and derivatives such as Stafib-1 as the first selective inhibitors of the STAT5b SH2 domain. Here, we demonstrate STAT5b binding of catechol bisphosphate by solid-state nuclear magnetic resonance, and report on rational optimization of Stafib-1 (K i = 44 nM) to Stafib-2 (K i = 9 nM). The binding site of Stafib-2 was validated using combined isothermal titration calorimetry (ITC) and protein point mutant analysis, representing the first time that functional comparison of wild-type versus mutant protein by ITC has been used to characterize the binding site of a small-molecule ligand of a STAT protein with amino acid resolution. The prodrug Pomstafib-2 selectively inhibits tyrosine phosphorylation of STAT5b in human leukaemia cells and induces apoptosis in a STAT5-dependent manner. We propose Pomstafib-2, which currently represents the most active, selective inhibitor of STAT5b activation available, as a chemical tool for addressing the fundamental question of which roles the different STAT5 proteins play in various cell processes. Transcription factors orchestrate cellular signalling by regulating transcription of their target genes, thus allowing precise regulation of cellular phenotype 1. They do not possess enzymatic activities, making their functional manipulation with cell-permeable small molecules more challenging. The transcription factors STAT5a and STAT5b in particular are highly homologous 2 and are frequently referred to jointly as " STAT5 " , implying that they carry out identical functions. However, while some protein functions are indeed redundant, others are not. For example, although both STAT5a and STAT5b are constitutively activated in numerous human cancers, including human leukaemias harbouring the Philadelphia chromosome 3 which leads to expression of the Bcr-Abl fusion protein, the inhibition of STAT5b was shown to reduce tumour cell proliferation more than the inhibition of STAT5a did 4, 5. Small-molecule inhibitors which differentiate between the two STAT5 proteins would be highly beneficial for clarifying their individual roles. The most effective and selective approach by which to inhibit STAT proteins involves functional inhibition of the protein-protein interaction domain, the Src homology 2 (SH2) domain 6, 7. However, for most STAT5 inhibitors developed to date, including chromone-based compounds 8, 9 , fos-fosal 10 , salicylic acid-based STAT5 inhibitors 11, 12 , an adenosine-5′-monophosphate derivative 13 , and an osmium complex 14 , selectivity for one STAT5 protein over the other was either minimal or not reported. We recently presented catechol bisphosphate (1, Fig. 1a) and its derivatives Stafib-1 (2, Table 1) 15 and Capstafin 16 as selective inhibitors of the STAT5b SH2 domain. Despite overwhelming evidence for the biological significance of STAT proteins as therapeutic targets, the development of small-molecule STAT inhibitors has been hampered by the absence of structural characterization of complexes between STATs and small molecules by X-ray crystallography or NMR spectroscopy. The isolated STAT SH2 domains are mostly insoluble; virtually all research studies to date have used STAT constructs containing multiple STAT domains 17, 18. The molecular weight of the proteins expressed by these constructs is too high for deducing detailed structural information of the STAT-ligand complex by solution-state NMR methods. We have employed solid-state NMR spectroscopy to verify and characterize the binding of catechol bisphos-phate to STAT5b, an interaction which provides the foundation of our docking-based model for the binding of Stafib-1 to STAT5b 15. Following structure-guided optimization of Stafib-1, we performed a comparative analysis

Several visualization tools for the mapping of protein-protein interactions have been developed in recent years. However, a systematic comparison of the virtues and limitations of different PPI visualization tools has not been carried out so far. In this study, we compare seven commonly used visualization tools, based on input and output file format, layout algorithm, database integration, Gene Ontology annotation and accessibility of each tool. The assessment was carried out based on brain disease datasets. Our suggested tools, NAViGaTOR, Cytoscape and Gephi perform competitively as PPI network visualization tools, can be a reference for future researches on PPI mapping and analysis.

Chromosome segregation is a crucial phenomenon in the cell cycle and defects in genome segregation result in an abnormality in various cellular events. Unlike higher eukaryotes, chromosome segregation and a number of cell cycle events are unusual in the protozoan parasite Entamoeba histolytica (E. histolytica). Characterization of Sir2 proteins from E. histolytica may reveal its unique cellular events as they play role in diverse cellular processes including chromosome segregation. E. histolytica has four homologs of Sir2 proteins. EhSir2a and EhSir2b show sequence similarity towards eukaryotic Sir2 homologs, whereas EhSir2c and EhSir2d are more like prokaryotic sirtuins. Using both computational and experimental methods, EhSir2c has been characterized in this study. The three-dimensional structure of EhSir2c is predicted by homology modelling. The protein interactors of EhSir2c have been identified by yeast-two-hybrid screening against the cDNA library of E. histolytica. We have identified a novel interactor, EhRAD23 which is a homolog of UV excision repair protein RAD23. The interaction of EhSir2c and EhRAD23 was validated by pull-down assay. UV-C irradiation up-regulates the relative expression of EhSir2c, suggesting the necessity of EhSir2c in UV-induced stress in this parasite.